Patient Prefer Adher
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Patient Prefer Adher · Jan 2023
Understanding Processes, Outcomes, and Contexts in Medication Adherence: The Medication Adherence Context and Outcomes (MACO) Framework.
Poor medication adherence is a significant problem, yet interventions to improve it have been largely ineffective. Existing ecological models indicate that adherence is multi-dimensional; however, they do not reflect understanding of context-specific processes and how they lead to adherence outcomes. A framework that reflects context-specific processes is important because it could be used to inform context-specific intervention delivery and measure associated adherence outcomes. ⋯ The MACO framework distinguishes context-specific processes and outcomes. The MACO framework may be useful to understand at which point(s) along the continuum people experience problems with managing medications. This understanding is potentially useful for developing and delivering context-specific interventions that are based on processes that underlie nonadherence and selecting adherence measures appropriate for the contexts.
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Patient Prefer Adher · Jan 2023
Attitudes Toward Providing Open Access for Use of Biospecimens and Health Records: A Cross-Sectional Study from Jordan.
Biospecimen repositories and big data generated from clinical research are critically important in advancing patient-centered healthcare. However, ethical considerations arising from reusing clinical samples and health records for subsequent research pose a hurdle for big-data health research. This study aims to assess the public's opinions in Jordan toward providing blanket consent for using biospecimens and health records in research. ⋯ The lack of public trust in Jordan toward data privacy is evident from this study. Therefore, a governance framework is needed to raise and maintain the public's trust in big-data research that warrants the future reuse of clinical samples and records. As such, the current study provides valuable insights that will inform the design of effective consent protocols required in data-intensive health research.
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Patient Prefer Adher · Jan 2023
Prevalence and Safety of Prescribing PPIs with Clopidogrel in Palestine.
Proton pump inhibitors (PPIs) are commonly prescribed medications that are thought to increase the risk of cardiovascular events because they reduce the effectiveness of clopidogrel via shared hepatic pathways. ⋯ In this study, we observed a high prevalence of prescribing a PPI in combination with clopidogrel, regardless of the FDA recommendations. No significant increase in cardiovascular events was observed in patients receiving concomitant clopidogrel and PPI therapy.
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Patient Prefer Adher · Jan 2023
Health-Related Quality of Life for Jordanian-Recovered Individuals During Post-COVID-19 Era: A Cross-Sectional Study.
This study aims to determine health-related quality of life (HRQoL) that includes the physical and mental health of recovered patients of COVID-19 and examines the significant impact of variables such as period of infection, sample demographics characteristics, hospitalization past, and chronic disease past and the other variables on HRQoL of COVID-19-recovered patients. ⋯ The HRQoL of COVID-19 patients was significantly impacted, independent of the period since hospitalization or rehabilitation. Policymakers and health workers should research strong ways to enhance the HRQoL of COVID-19 patients as soon as possible. Elderly patients and those who have been infected more than one time and being hospitalized have a greater probability of decreased HRQoL after infection.
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Patient Prefer Adher · Jan 2023
A Quantitative Framework for Medication Non-Adherence: Integrating Patient Treatment Expectations and Preferences.
Medication non-adherence remains a significant challenge in healthcare, impacting treatment outcomes and the overall effectiveness of medical interventions. This article introduces a novel approach to understanding and predicting medication non-adherence by integrating patient beliefs, efficacy expectations, and perceived costs. Existing theoretical models often fall short in quantifying the impact of barrier removal on medication adherence and struggle to address cases where patients consciously choose not to follow prescribed medication regimens. In response to these limitations, this study presents an empirical framework that seeks to provide a quantifiable model for both individual and population-level prediction of non-adherence under different scenarios. ⋯ Our framework represents a pioneering effort to quantitatively link non-adherence to patient preferences. Preliminary results from our pilot study of patients with hypertension suggest that the framework offers a viable alternative for evaluating the potential impact of interventions on treatment adherence.